> ## Documentation Index
> Fetch the complete documentation index at: https://docs.evidentlyai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Work with datasets

> How to create, upload and manage Datasets.

<Tip>
  You must first connect to [Evidently Cloud](/docs/setup/cloud) or local workspace and [create a Project](/docs/platform/projects_manage).
</Tip>

## Upload a Dataset

<Tabs>
  <Tab title="Python">
    Prepare your dataset as an Evidently Dataset with the corresponding data definition. To upload a Dataset to the specified Project in workspace `ws`, use the `add_dataset` method:

    ```python theme={null}
    eval_data = Dataset.from_pandas(
        source_df,
        data_definition=DataDefinition()
    )
    ws.add_dataset(
        dataset = eval_data, 
        name = "dataset_name",
        project_id = project.id, 
        description = "Optional description")
    ```

    You must always specify the dataset `name` that you will see in the UI. The description is optional.
  </Tab>

  <Tab title="UI">
    To upload any existing dataset as a CSV file, click on "Add dataset". When you upload the Dataset, you must also add a [**data definition**](/docs/library/data_definition). This lets Evidently understand the role of specific columns and prepare your Dataset for future evaluations.
  </Tab>
</Tabs>

<Note>
  **How to create an Evidently Dataset?** Read the [Data Definition docs](../library/data-definition).
</Note>

## Download the Dataset

You can pull the Dataset stored or generated on the platform to your local environment. For example, call the evaluation or tracing dataset to use in a CI/CD testing script.

Use the `load_dataset` method:

```python theme={null}
eval_dataset = ws.load_dataset(dataset_id = "YOUR_DATASET_ID") 

#to create as pandas dataframe
df = eval_dataset.as_dataframe()
```

## Include the Dataset

You can include Datasets when you upload Reports to the platform. This way, after running an evaluation locally you simultaneously upload:

* the Report with evaluation result,
* the Dataset it was generated for, with new added scores if applicable.

By default, you upload only the Report.

To include the Dataset, use the `include_data` parameter:

```python theme={null}
ws.add_run(project.id, data_report, include_data=True)
```

Check the docs on [running evals via API](/docs/platform/evals_api) for details.